ORCID Profile
0000-0003-4809-8441
Current Organisation
Macquarie University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Natural Language Processing | Linguistic Processes (incl. Speech Production and Comprehension) | Knowledge Representation and Machine Learning | Cognitive Science | Computational Linguistics | Photonics and Electro-Optical Engineering (excl. Communications) | Quantum Chemistry | Genetics | Artificial Intelligence and Image Processing | Nanophotonics | Nanotechnology not elsewhere classified | Climate Change Processes | Turbulent Flows | Genomics | Theoretical and Computational Chemistry not elsewhere classified | Interdisciplinary Engineering | Bioinformatics
ANZSRC Socio-Economic Objective (SEO)
Expanding Knowledge in the Information and Computing Sciences | Expanding Knowledge in Language, Communication and Culture | Expanding Knowledge in Psychology and Cognitive Sciences | Education and Training not elsewhere classified | Information and Communication Services not elsewhere classified | Child Health | Expanding Knowledge in the Chemical Sciences | Expanding Knowledge in the Physical Sciences | Expanding Knowledge in the Biological Sciences |
Related Links
Publications
Memoization of coroutined constraints
Publisher: Association for Computational Linguistics
Date: 1995
Active learning for deep semantic parsing
Publisher: Association for Computational Linguistics
Date: 2018
DOI: 10.18653/V1/P18-2008
Exemplar-based learning probably requires learning abstractions: A commentary on Ambridge (2020)
Publisher: SAGE Publications
Date: 07-02-2020
Abstract: Exemplar-based learning requires (i) a segmentation procedure for identifying the units of past experiences that a present experience can be compared to, and (ii) a similarity function for comparing these past experiences to the present experience. This article argues that for a learner to learn a language these two mechanisms will require abstractions such as linguistic features. Moreover, because the segmentation procedure will vary depending on the language, a radical exemplar theory is likely to require linguistic abstractions for learning.
A Computationally Efficient Algorithm for Learning Topical Collocation Models
Publisher: Association for Computational Linguistics
Date: 2015
DOI: 10.3115/V1/P15-1141
A GPSG account of VP structure in German
Publisher: Walter de Gruyter GmbH
Date: 1986
Learning Typed Entailment Graphs with Global Soft Constraints
Publisher: MIT Press - Journals
Date: 12-2018
DOI: 10.1162/TACL_A_00250
Abstract: This paper presents a new method for learning typed entailment graphs from text. We extract predicate-argument structures from multiple-source news corpora, and compute local distributional similarity scores to learn entailments between predicates with typed arguments (e.g., person contracted disease). Previous work has used transitivity constraints to improve local decisions, but these constraints are intractable on large graphs. We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph. Learning takes only a few hours to run over 100K predicates and our results show large improvements over local similarity scores on two entailment data sets. We further show improvements over paraphrases and entailments from the Paraphrase Database, and prior state-of-the-art entailment graphs. We show that the entailment graphs improve performance in a downstream task.
An Improved Non-monotonic Transition System for Dependency Parsing
Publisher: Association for Computational Linguistics
Date: 2015
DOI: 10.18653/V1/D15-1162
Probabilistic Grammars and their Applications
Publisher: Elsevier
Date: 2015
Passive Target Localization by Asynchronous Self-Locating Receivers in Multipath Environments
Publisher: IEEE
Date: 12-2019
Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection
Publisher: JMIR Publications Inc.
Date: 29-08-2016
DOI: 10.2196/JMIR.6045
Abstract: In public health surveillance, measuring how information enters and spreads through online communities may help us understand geographical variation in decision making associated with poor health outcomes. Our aim was to evaluate the use of community structure and topic modeling methods as a process for characterizing the clustering of opinions about human papillomavirus (HPV) vaccines on Twitter. The study examined Twitter posts (tweets) collected between October 2013 and October 2015 about HPV vaccines. We tested Latent Dirichlet Allocation and Dirichlet Multinomial Mixture (DMM) models for inferring topics associated with tweets, and community agglomeration (Louvain) and the encoding of random walks (Infomap) methods to detect community structure of the users from their social connections. We examined the alignment between community structure and topics using several common clustering alignment measures and introduced a statistical measure of alignment based on the concentration of specific topics within a small number of communities. Visualizations of the topics and the alignment between topics and communities are presented to support the interpretation of the results in context of public health communication and identification of communities at risk of rejecting the safety and efficacy of HPV vaccines. We analyzed 285,417 Twitter posts (tweets) about HPV vaccines from 101,519 users connected by 4,387,524 social connections. Examining the alignment between the community structure and the topics of tweets, the results indicated that the Louvain community detection algorithm together with DMM produced consistently higher alignment values and that alignments were generally higher when the number of topics was lower. After applying the Louvain method and DMM with 30 topics and grouping semantically similar topics in a hierarchy, we characterized 163,148 (57.16%) tweets as evidence and advocacy, and 6244 (2.19%) tweets describing personal experiences. Among the 4548 users who posted experiential tweets, 3449 users (75.84%) were found in communities where the majority of tweets were about evidence and advocacy. The use of community detection in concert with topic modeling appears to be a useful way to characterize Twitter communities for the purpose of opinion surveillance in public health applications. Our approach may help identify online communities at risk of being influenced by negative opinions about public health interventions such as HPV vaccines.
Idea density for predicting Alzheimer's disease from transcribed speech
Publisher: Association for Computational Linguistics
Date: 2017
DOI: 10.18653/V1/K17-1033
Joint and conditional estimation of tagging and parsing models
Publisher: Association for Computational Linguistics
Date: 2001
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
Publisher: Association for Computational Linguistics
Date: 2017
DOI: 10.18653/V1/K17-3014
Guided Open Vocabulary Image Captioning with Constrained Beam Search
Publisher: Association for Computational Linguistics
Date: 2017
DOI: 10.18653/V1/D17-1098
The DOP Estimation Method Is Biased and Inconsistent
Publisher: MIT Press - Journals
Date: 03-2002
DOI: 10.1162/089120102317341783
Abstract: A data-oriented parsing or DOP model for statistical parsing associates fragments of linguistic representations with numerical weights, where these weights are estimated by normalizing the empirical frequency of each fragment in a training corpus (see Bod [1998] and references cited therein). This note observes that this estimation method is biased and inconsistent that is, the estimated distribution does not in general converge on the true distribution as the size of the training corpus increases.
Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model
Publisher: Association for Computational Linguistics
Date: 2017
DOI: 10.18653/V1/P17-2087
Topic Models with Topic Ordering Regularities for Topic Segmentation
Publisher: IEEE
Date: 12-2015
DOI: 10.1109/ICDM.2014.49
Uncovering the development of linguistic knowledge in lesser studied languages
Publisher: Cambridge University Press (CUP)
Date: 16-03-2023
DOI: 10.1017/S0305000922000691
Abstract: There has recently been an increased interest in studying the language development of non-western languages. This is not new - it began in 1960’s and continued into the 1980’s and 1990’s. The current renewed interest is much welcomed, and will benefit from many of the experimental methods and theoretical insights developed over the past decades.
SPICE: Semantic propositional image caption evaluation
Publisher: Springer International Publishing
Date: 2016
Integrating Lexical Information into Entity Neighbourhood Representations for Relation Prediction
Publisher: Association for Computational Linguistics
Date: 2021
Features and agreement
Publisher: Association for Computational Linguistics
Date: 1995
Efficient techniques for parsing with tree automata
Publisher: Association for Computational Linguistics
Date: 2016
DOI: 10.18653/V1/P16-1192
Neighborhood Mixture Model for Knowledge Base Completion
Publisher: Association for Computational Linguistics
Date: 2016
DOI: 10.18653/V1/K16-1005
Lexicalized stochastic modeling of constraint-based grammars using log-linear measures and EM training
Publisher: Association for Computational Linguistics
Date: 2000
Syllable weight encodes mostly the same information for English word segmentation as dictionary stress
Publisher: Association for Computational Linguistics
Date: 2014
DOI: 10.3115/V1/D14-1091
A resource sensitive reinterpretation of lexical-functional grammar
Publisher: Springer Science and Business Media LLC
Date: 1999
Predicting accuracy on large datasets from smaller pilot data
Publisher: Association for Computational Linguistics
Date: 2018
DOI: 10.18653/V1/P18-2072
Edit detection and parsing for transcribed speech
Publisher: Association for Computational Linguistics
Date: 2001
Parsing and disfluency placement
Publisher: Association for Computational Linguistics
Date: 2002
Expressing disjunctive and negative feature constraints with classical first-order logic
Publisher: Association for Computational Linguistics
Date: 1990
Parsing and Its Applications for Conversational Speech
Publisher: IEEE
Date: 2005
Introduction to the Special Issue on New Approaches to Statistical Speech and Text Processing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2008
Interaction between Discourse Functions and Agreement in Setawana
Publisher: Walter de Gruyter GmbH
Date: 1989
Parsing as deduction: The use of knowledge of language
Publisher: Springer Science and Business Media LLC
Date: 1989
DOI: 10.1007/BF01069050
Language modeling using efficient best-first bottom-up parsing
Publisher: IEEE
Date: 2003
Estimators for stochastic Unification-Based grammars
Publisher: Association for Computational Linguistics
Date: 1999
Statistical models of syntax learning and use
Publisher: Wiley
Date: 05-2002
Meeting of the Association for Symbolic Logic, Stanford, California, 1985
Publisher: Cambridge University Press (CUP)
Date: 09-1986
DOI: 10.2307/2274046
A Bayesian framework for word segmentation: Exploring the effects of context
Publisher: Elsevier BV
Date: 07-2009
DOI: 10.1016/J.COGNITION.2009.03.008
Abstract: Since the experiments of Saffran et al. [Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning in 8-month-old infants. Science, 274, 1926-1928], there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify in idual words. In this work, we use computational modeling to explore the effects of different assumptions the learner might make regarding the nature of words--in particular, how these assumptions affect the kinds of words that are segmented from a corpus of transcribed child-directed speech. We develop several models within a Bayesian ideal observer framework, and use them to examine the consequences of assuming either that words are independent units, or units that help to predict other units. We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g. what's that, do you, in the house) misidentified as in idual words. In contrast, when the learner assumes that words are predictive, the resulting segmentation is far more accurate. These results indicate that taking context into account is important for a statistical word segmentation strategy to be successful, and raise the possibility that even young infants may be able to exploit more subtle statistical patterns than have usually been considered.
Recognizing disfluencies in conversational speech
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2006
An adaptable task-oriented dialog system for stand-alone embedded devices
Publisher: Association for Computational Linguistics
Date: 2019
DOI: 10.18653/V1/P19-3009
Duality of Link Prediction and Entailment Graph Induction
Publisher: Association for Computational Linguistics
Date: 2019
DOI: 10.18653/V1/P19-1468
AMR dependency parsing with a typed semantic algebra
Publisher: Association for Computational Linguistics
Date: 2018
DOI: 10.18653/V1/P18-1170
A discovery procedure for certain phonological rules
Publisher: Association for Computational Linguistics
Date: 1984
The Computational Complexity of GLR Parsing
Publisher: Springer US
Date: 1991
Marr’s levels and the minimalist program
Publisher: Springer Science and Business Media LLC
Date: 07-2016
DOI: 10.3758/S13423-016-1062-1
Abstract: A simple change to a cognitive system at Marr's computational level may entail complex changes at the other levels of description of the system. The implementational level complexity of a change, rather than its computational level complexity, may be more closely related to the plausibility of a discrete evolutionary event causing that change. Thus the formal complexity of a change at the computational level may not be a good guide to the plausibility of an evolutionary event introducing that change. For ex le, while the Minimalist Program's Merge is a simple formal operation (Berwick & Chomsky, 2016), the computational mechanisms required to implement the language it generates (e.g., to parse the language) may be considerably more complex. This has implications for the theory of grammar: theories of grammar which involve several kinds of syntactic operations may be no less evolutionarily plausible than a theory of grammar that involves only one. A deeper understanding of human language at the algorithmic and implementational levels could strengthen Minimalist Program's account of the evolution of language.
Sign constraints on feature weights improve a joint model of word segmentation and phonology
Publisher: Association for Computational Linguistics
Date: 2015
DOI: 10.3115/V1/N15-1034
Deductive parsing with multiple levels of representation
Publisher: Association for Computational Linguistics
Date: 1988
Weighted and Probabilistic Context-Free Grammars Are Equally Expressive
Publisher: MIT Press - Journals
Date: 12-2007
DOI: 10.1162/COLI.2007.33.4.477
Abstract: This article studies the relationship between weighted context-free grammars (WCFGs), where each production is associated with a positive real-valued weight, and probabilistic context-free grammars (PCFGs), where the weights of the productions associated with a nonterminal are constrained to sum to one. Because the class of WCFGs properly includes the PCFGs, one might expect that WCFGs can describe distributions that PCFGs cannot. However, Z. Chi (1999, Computational Linguistics, 25(1):131–160) and S. P. Abney, D. A. McAllester, and P. Pereira (1999, In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pages 542–549, College Park, MD) proved that every WCFG distribution is equivalent to some PCFG distribution. We extend their results to conditional distributions, and show that every WCFG conditional distribution of parses given strings is also the conditional distribution defined by some PCFG, even when the WCFG's partition function erges. This shows that any parsing or labeling accuracy improvement from conditional estimation of WCFGs or conditional random fields (CRFs) over joint estimation of PCFGs or hidden Markov models (HMMs) is due to the estimation procedure rather than the change in model class, because PCFGs and HMMs are exactly as expressive as WCFGs and chain-structured CRFs, respectively.
Contextual dependencies in unsupervised word segmentation
Publisher: Association for Computational Linguistics
Date: 2006
Effectiveness of a machine-level, global optimizer
Publisher: Association for Computing Machinery (ACM)
Date: 07-1986
DOI: 10.1145/13310.13321
Abstract: We present an overview of the design of a machine-code-level, global (intraprocedural) optimizer that supports several front-ends producing code for the Hewlett-Packard Precision Architecture family of machines. The basic optimization strategy is described, including information about the ision of responsibilities between various components of the compiler. Optimization algorithms are described, including a discussion of the dataflow information they require. Measurements showing the collective and in idual effects of various optimizer components are presented. The performance data presented here was collected using a preliminary version of the optimizer. Development is continuing and further improvements are expected.
Unsupervised Text Segmentation Based on Native Language Characteristics
Publisher: Association for Computational Linguistics
Date: 2017
DOI: 10.18653/V1/P17-1134
Mathematical Foundations of Speech and Language Processing
Publisher: Springer New York
Date: 2004
Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments
Publisher: IEEE
Date: 06-2018
Two ways of formalizing grammars
Publisher: Springer Science and Business Media LLC
Date: 06-1994
DOI: 10.1007/BF00985036
The forest for the trees
Publisher: Elsevier BV
Date: 09-2014
Efficient probabilistic top-down and left-corner parsing
Publisher: Association for Computational Linguistics
Date: 1999
Mention Flags (MF): Constraining Transformer-based Text Generators
Publisher: Association for Computational Linguistics
Date: 2021
Multilingual semantic parsing and code-switching
Publisher: Association for Computational Linguistics
Date: 2017
DOI: 10.18653/V1/K17-1038
Computer aids for comparative dictionaries
Publisher: Walter de Gruyter GmbH
Date: 1985
Bottom-up and top-down attention for image captioning and visual question answering
Publisher: IEEE
Date: 06-2018
An Incremental Algorithm for Transition-based CCG Parsing
Publisher: Association for Computational Linguistics
Date: 2015
DOI: 10.3115/V1/N15-1006
Effective self-training for parsing
Publisher: Association for Computational Linguistics
Date: 2006
STransE: a novel embedding model of entities and relationships in knowledge bases
Publisher: Association for Computational Linguistics
Date: 2016
DOI: 10.18653/V1/N16-1054
LOGICAL EMBEDDED PUSH‐DOWN AUTOMATA IN TREE‐ADJOINING GRAMMAR PARSING
Publisher: Wiley
Date: 11-1994
Using Left-corner Parsing to Encode Universal Structural Constraints in Grammar Induction
Publisher: Association for Computational Linguistics
Date: 2016
DOI: 10.18653/V1/D16-1004
A summary of the 2012 JHU CLSP workshop on zero resource speech technologies and models of early language acquisition
Publisher: IEEE
Date: 05-2013
How to Best Use Syntax in Semantic Role Labelling
Publisher: Association for Computational Linguistics
Date: 2019
DOI: 10.18653/V1/P19-1529
Memory requirements and local ambiguities of parsing strategies
Publisher: Springer Science and Business Media LLC
Date: 05-1991
DOI: 10.1007/BF01067217
nocaps: novel object captioning at scale
Publisher: IEEE
Date: 10-2019
Modelling function words improves unsupervised word segmentation
Publisher: Association for Computational Linguistics
Date: 2014
DOI: 10.3115/V1/P14-1027
WordSeg: Standardizing unsupervised word form segmentation from text
Publisher: Springer Science and Business Media LLC
Date: 04-2019
DOI: 10.3758/S13428-019-01223-3
Abstract: A basic task in first language acquisition likely involves discovering the boundaries between words or morphemes in input where these basic units are not overtly segmented. A number of unsupervised learning algorithms have been proposed in the last 20 years for these purposes, some of which have been implemented computationally, but whose results remain difficult to compare across papers. We created a tool that is open source, enables reproducible results, and encourages cumulative science in this domain. WordSeg has a modular architecture: It combines a set of corpora description routines, multiple algorithms varying in complexity and cognitive assumptions (including several that were not publicly available, or insufficiently documented), and a rich evaluation package. In the paper, we illustrate the use of this package by analyzing a corpus of child-directed speech in various ways, which further allows us to make recommendations for experimental design of follow-up work. Supplementary materials allow readers to reproduce every result in this paper, and detailed online instructions further enable them to go beyond what we have done. Moreover, the system can be installed within container software that ensures a stable and reliable environment. Finally, by virtue of its modular architecture and transparency, WordSeg can work as an open-source platform, to which other researchers can add their own segmentation algorithms.
Reranking and self-training for parser adaptation
Publisher: Association for Computational Linguistics
Date: 2006
Finite-state approximation of constraint-based grammars using left-corner grammar transforms
Publisher: Association for Computational Linguistics
Date: 1998
Search Personalization with Embeddings
Publisher: Springer International Publishing
Date: 2017
Parsing with discontinuous constituents
Publisher: Association for Computational Linguistics
Date: 1985
Effective use of prosody in parsing conversational speech
Publisher: Association for Computational Linguistics
Date: 2005
Related Organisations
Related Funding Activities
Discovery Projects - Grant ID: DP110102506
Start Date: 07-2011
End Date: 06-2016
Amount: $368,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP110102593
Start Date: 07-2011
End Date: 12-2015
Amount: $395,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP160102156
Start Date: 06-2016
End Date: 12-2020
Amount: $388,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Infrastructure, Equipment And Facilities - Grant ID: LE140100002
Start Date: 2014
End Date: 12-2016
Amount: $1,025,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Infrastructure, Equipment And Facilities - Grant ID: LE110100143
Start Date: 08-2011
End Date: 08-2012
Amount: $500,000.00
Funder: Australian Research Council
View Funded Activity